Generative AI Models: A Detailed Guide

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Generative artificial intelligence (GenAI) models are powerful AI platforms that generate various outputs based on massive training data sets, neural networks, deep learning architecture, and user prompts. Depending on the type of generative AI model you’re working with, you can generate images, translate text into images, synthesize speech and audio, create original content, and generate synthetic data. New AI companies and tools are continuously emerging, and genetic AI models are impacting businesses in different fields. Here’s what you need to know about generative AI models and how they’re trained, their benefits and challenges, and the best use cases to help your business.

How Do Generative AI Models Work?

Generative AI models are massive, big data-driven models that power the emerging artificial intelligence technology that can create content. Using unsupervised or semi-supervised learning methods, generative AI models are trained to recognize small-scale and overarching patterns and relationships in training datasets that come from all kinds of sources, including the internet, wikis, books, image libraries, and more. This training enables a generative AI model to mimic those patterns when generating new content, making it believable that the content could have been created by or belonged to a human rather than a machine.

Generative AI models can closely replicate actual human content because they are designed with layers of neural networks that emulate the synapses between neurons in a human brain. When the neural network design is combined with large training datasets, complex deep learning and training algorithms, and frequent re-training and updates, these models can improve and “learn” over time and at scale. Here are different AI technologies and methods used to create generative AI models:

Neural Networks and Deep Learning

Neural networks and deep learning are both fundamental concepts in the field of AI, often used interchangeably. However, subtle differences between the two terms mark their distinctions.

Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes or neurons organized into layers designed to signal to one another. Neural networks learn by adjusting the weights and biases of these connections through a process called backpropagation. One of the most popular examples of a neural network is Google’s search algorithm, which is probably the largest in existence.

Deep learning, on the other hand, is a subset of machine learning that uses neural networks with multiple hidden layers. These multilayered neural networks, called deep neural networks, allow models to learn complex patterns and relationships in data and simulate the decision-making power of the human brain. Deep learning solves various problems, including image recognition, natural language processing, and speech recognition.

Training Methods: Unsupervised and Semi-Supervised Learning

Unsupervised learning is a type of machine learning method that learns from data without human supervision. It enables AI to train on its own in unlabeled data and identify patterns, structures, and relationships within the data. Unsupervised learning is useful when labeled data is scarce or difficult to obtain, making it ideal for exploratory data analysis, customer segmentation, and image recognition. In generative AI, unsupervised learning enables you to apply a full spectrum of machine learning algorithms to raw data and further enhance the performance of generative AI models.

Semi-supervised learning combines supervised and unsupervised learning. In this method, a small portion of training data is labeled, while the rest remains unlabeled. Semi-supervised learning methods are relevant in situations where obtaining a sufficient amount of labeled data is difficult while a large amount of unlabeled data is easily accessible. The labeled data helps the AI model to learn initial patterns, and it can use these patterns to make predictions on the unlabeled data.

Data Sources and Pattern Recognition

Generative AI models rely heavily on vast amounts of data to learn patterns and produce new content. The quality and diversity of the data sources, including text, image, audio, and video, significantly impact the model’s performance and output quality. Generative AI models are adept at recognizing patterns within the data they’re trained on. AI models can identify hidden relationships among the data and even learn their statistical distribution. Additionally, AI models can capture the unique characteristics of the data, such as the tone of a voice actor, the writing style of an author, or the artistic style of a painter.

8 Types of Generative AI Models

Many types of generative AI models are in operation today, and the number continues to grow as AI experts experiment with existing models. Among the many types of generative AI models are text-to-text generators, text-to-image generators, image-to-image generators, and image-to-text generators. It’s possible for a model to fit into multiple categories—for example, the latest updates to ChatGPT and GPT-4 make it a transformer-based, large language, and multimodal model. Some of the most common types include the following:

• Generative Adversarial Networks (GANs): Best for image duplication and synthetic data generation.
• Transformer-Based Models: Best for text generation and content/code completion—subsets include generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) models.
• Diffusion Models: Best for image generation and video/image synthesis.
• Variational Autoencoders (VAEs): Best for image, audio, and video content creation, especially when synthetic data needs to be photorealistic.
• Unimodal Models: Models that are set up to accept only one data input format; most generative AI models today are unimodal models.
• Multimodal Models: Designed to accept multiple types of inputs and prompts when generating outputs—for example, GPT-4 can accept both text and images as inputs.
• Large Language Models: Designed to generate and complete written content at scale, these are the most popular and well-known type of generative AI model right now.
• Neural Radiance Fields (NeRFs): Emerging neural network technology that can be used to generate 3D imagery based on 2D image inputs.

Generative AI vs. Discriminative AI Models

The primary difference between generative and discriminative AI models is that generative AI models can create new content and outputs based on their training. Discriminative modeling, on the other hand, is primarily used to classify existing data through supervised learning. As an example, a protein classification tool would operate on a discriminative model, while a protein generator would run on a generative AI model.

Generative AI vs. Predictive AI Models

Generative models are designed to create something new, while predictive AI models are set up to make predictions based on data that already exists. Continuing with the example above, a tool that predicts the next segment of amino acids in a protein molecule would work through a predictive AI model, while a protein generator requires a generative AI model approach.

Read Generative AI vs. Predictive AI to gain a fuller understanding of the differences between generative and predictive models.

How Are Generative AI Models Trained?

Training generative AI models is a complex process that involves several key steps, including selecting the model architecture, collecting and preparing the data, and training the model.

Step 1: Select the Right Model Architecture

An AI model is designed to emulate human thought processes, problem-solving, decision-making, and pattern recognition through the processing of diverse data sources. A model’s architecture establishes its fundamental framework and helps you control how the model learns from data and generates new content. Your choice of model architecture depends on the specific task and the type of data being used. After choosing a model architecture, you should carefully adjust hyperparameters, as they can significantly impact the AI model’s performance.

Step 2: Collect and Prepare the Data

Without proper data collection and preparation, AI models will not be able to effectively analyze and interpret data. In gathering data, obtain a large and diverse data set representative of the task your AI model is intended to perform. After data acquisition, clean and preprocess it to remove incorrect, corrupted, duplicate, or incomplete data. This way, you can ensure that your AI model is trained on high-quality data. Techniques like data augmentation also help in increasing the diversity of the training data and creating new, synthetic data points.

Step 3: Train the Process and Algorithms

One of the most important steps in training an AI model is to define the loss function, which measures the difference between the model’s prediction and the actual data. For numerical prediction, mean square error (MSE) is used, while cross-entropy loss is suitable for categorical predictions. Minimize the loss function using gradient descent or the Adam optimization algorithm to update the model’s parameters and improve its accuracy over time.

Step 4: Evaluate and Iterate the Model’s Performance

Assess your AI model’s performance using appropriate metrics such as inception score (IS) for evaluating the quality of an image and Fréchet inception distance (FID) for quantifying the realism of GAN-generated images. Also experiment with different hyperparameters, including learning and batch size, to further tune them and find the optimal configuration. If the evaluation results are not satisfactory, refine the data, model architecture, or training processes and continuously iterate or repeat the evaluation process.

Training Different Types of Generative AI

While the overall process follows the steps outlined above, different types of generative AI models—including transformer-based, generative adversarial, and diffusion models—are all trained a little differently.

Transformer-Based Model Training

Transformer-based models are designed with massive neural networks and transformer infrastructure that make it possible for the model to recognize and remember relationships and patterns in sequential data. To start, these models are trained to look through, store, and “remember” large datasets from a variety of sources and, sometimes, in a variety of formats. Training data sources could be websites and online texts, news articles, wikis, books, image and video collections, and other large bodies of data that provide valuable information.

From there, transformer models can contextualize all of this data and effectively focus on the most important parts of the training dataset through that learned context. The sequences this type of model recognizes from its training will inform how it responds to user prompts and questions.

Essentially, transformer-based models pick the next most logical piece of data to generate in a sequence of data. Encoders and/or decoders are built into the platform to decode the tokens or blocks of content that have been segmented based on user inputs.

GAN Model Training

GAN (generative adversarial network) models are trained with two different sub-model neural networks: a generator and a discriminator. The generator generates content based on user inputs and training data, while the discriminator model evaluates generated content against “real” examples to determine which output is real or accurate.

First, the generator creates new “fake” data based on a randomized noise signal. Then, the discriminator blindly compares that fake data to real data from the model’s training data to determine which data is “real” or the original data. The two sub-models cycle through this process repeatedly until the discriminator is no longer able to find flaws or differences in the newly generated data compared to the training data.

Diffusion Model Training

Diffusion models require both forward training and reverse training, or forward diffusion and reverse diffusion. The forward diffusion process involves adding randomized noise to training data. The model is trained to generate outputs using the noisy data (not as refined or specific) as input. The noise introduces variations and perturbations in the data, making the model robust and helping it to learn different possible outputs for a given input.

When the reverse diffusion process begins, noise is slowly removed or reversed from the dataset to generate content that matches the original’s qualities. This process encourages the model to focus on the underlying structure and patterns in the data, rather than relying on the noise to produce the desired outputs. By gradually removing the noise, the model learns to produce outputs that closely match the desired qualities of the original input data.

Noise, in this case, is best defined as signals that cause behaviors you don’t want to keep in your final dataset but that help you to gradually distinguish between correct and incorrect data inputs and outputs.

Benefits of Generative AI Models

Generative AI models offer various benefits essential to the future of artificial intelligence. The following are some of the most common applications of generative AI:

• Data Augmentation: Generative models can be used to augment datasets by generating synthetic data. This is helpful in scenarios where sufficient real-world labeled data is not available, making it useful for training other machine learning models.
• Synthetic Data Generation: Generative AI models can create new datasets that imitate actual customer data without compromising privacy, as they don’t contain real-world information. You can generate synthetic data to meet the needs of a certain application or industry in cases where real-world data is scarce or difficult to obtain.
• Natural Language Processing: Generative AI models can be used to create AI chatbots and virtual AI assistants capable of understanding and generating human-like responses in natural language. They can generate human-like text for content creation, including articles, stories, and more.
• Image and Video Synthesis: Generative AI allows artists, designers, and businesses to generate images and synthetic video content and reduce production costs. Generative AI models can create highly realistic content useful for various purposes, such as novel patterns, special effects in movies or gaming, or design inspirations. You can also customize these models to your specific style and preferences, allowing you to generate content that aligns with your brand and vision. 
• Creative and Artistic Uses: Generative AI models can be used to create art, poetry, music, and other artistic works. For example, OpenAI’s Jukebox, a generative model, can compose music in different genres. Generative AI models can also be leveraged for content synthesis, as they are capable of producing diverse and creative content and assisting in brainstorming and ideation processes.
• Versatility in Various Domains: AI models can be fine-tuned for various tasks, such as translation, summarization, and question-answering. They can also be adapted to different domains and industries with proper training and fine-tuning. For example, depending on the tuning, the output can be very serious and proper, or casual and recreational. The mode and mood of the output can be tuned to a remarkably specific degree.

Learn more about generative AI real-world use cases in our list of key generative AI examples in 2024.

Challenges in Generative AI Models

Generative AI models, while powerful and versatile, also face challenges that limit their capabilities. These challenges arise due to the inherent complexity of the tasks the models are intended to perform, the limitations of current methods, and the ethical implications of AI usage. The following are some of the most common challenges of generative AI models:

• Mode Collapse in GANs: GANs may suffer from mode collapse, which is when the generator learns to fool the discriminator by producing a limited set of outputs, ignoring the diversity present in the training data. This can result in repetitive or less varied generated content.
• Training Complexity: Generative models often require large amounts of data and computational resources for training, a resource-intensive nature of training that limits accessibility for smaller research labs and individual researchers. It also requires domain-specific knowledge, as a lack of domain-specific expertise can result in the AI model giving suboptimal outputs, or even outputting AI hallucinations.
• Adversarial Attacks: Generative models, especially GANs, are susceptible to adversarial attacks where small perturbations to input data can lead to unexpected or malicious outputs. Learning to effectively combat adversarial attacks is an active area of research.
• Fine-Tuning and Transfer Learning: Adapting pre-trained generative models to specific tasks or domains may be challenging. The ability to fine-tune without causing catastrophic forgetting or degradation in performance is another ongoing research concern and requires more work and investment.

Examples of Generative AI Models

Many generative AI vendors build their tools using a popular existing model as the foundation. For example, many of Microsoft’s new Copilot tools run on GPT-4 from OpenAI. Below you’ll find some of the most popular generative AI models available today:

• GPT-X: GPT-3, GPT-3.5, and GPT-4 are different generations of the GPT foundation model managed, owned, and created by OpenAI. The latest version, GPT4, uses a multimodal LLM that is the basis for ChatGPT.
• OpenAI Codex: Another model from OpenAI, Codex is able to generate code and autocomplete code in response to natural language prompts. It is the foundation model for tools like GitHub Copilot.
• Stable Diffusion: One of the most popular diffusion models, Stability AI’s Stable Diffusion is primarily used for text-to-image generation.
• LaMDA: A transformer-based model from Google, LaMDA is designed to support conversational use cases.
• PaLM: Another transformer-based LLM from Google, PaLM is designed to support multilingual content generation and coding. PaLM 2 is the latest version of the model and is the foundation for Google Bard.
• AlphaCode: A developer and coding support tool from DeepMind, AlphaCode is a large language model that generates code based on natural language inputs and questions.
• BLOOM: Hugging Face’s BLOOM is an autoregressive, multilingual LLM that mostly focuses on completing statements with missing text or strings of code with missing code.
• LLaMA: LLaMA is a smaller large language model option from Meta, with a goal of making generative AI models more accessible to users with fewer infrastructural resources.
• Midjourney: Midjourney is a generative AI model that operates similarly to Stable Diffusion, generating imagery from natural language prompts that users submit.

Learn how AI’s development is driving the growth of AI models in our review of current and future trends in the generative AI landscape.

Bottom Line: Making the Most Out of Generative AI Models

Generative AI models are highly scalable, accessible artificial intelligence solutions that are rightfully getting publicity as they supplement and transform various business operations. However, there are many concerns about how these tools work, their lack of transparency and built-in security safeguards, and generative AI ethics in general. Whether your organization is working to develop a generative AI model, build off of a foundational model, or simply use ChatGPT for daily tasks, keep in mind that the best way to use generative AI models is with comprehensive employee and customer training and clear ethical use policies in place.

To learn about businesses and organizations actively developing generative AI models, see our guide to the top generative AI companies.